Senior Manager, Data Science - AI Foundations

Capital One Capital One · Banking · McLean, VA +2

Senior Manager, Data Science - AI Foundations at Capital One. This role focuses on building and shipping AI/ML solutions for the company's mobile app, leveraging technologies like PyTorch, AWS, Hugging Face, LangChain, and VectorDBs. The position involves adapting and fine-tuning LLMs for customer-facing applications, building ML and NLP models through all development phases, and operationalizing them in production systems serving over 80 million customers. The ideal candidate has experience in training language models, computer vision models, and expertise in areas like training optimization, self-supervised learning, explainability, and RLHF, with a track record of delivering models at scale.

What you'd actually do

  1. Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI powered products that change how customers interact with their money.
  2. Leverage a broad stack of technologies — Pytorch, AWS Ultraclusters, Hugging Face, LangChain, Lightning, VectorDBs, and more — to reveal the insights hidden within huge volumes of numeric and textual data.
  3. Be the expert in Natural Language Processing (NLP) to harness the power of Large Language Models (LLMs), adapt and finetune them for customer facing applications and features.
  4. Build machine learning and NLP models through all phases of development, from design through training, evaluation, and validation; partnering with engineering teams to operationalize them in scalable and resilient production systems that serve 80+ million customers.
  5. Flex your interpersonal skills to translate the complexity of your work into tangible business goals.

Skills

Required

  • Python
  • SQL
  • Machine Learning
  • Data Analytics
  • Large Language Models (LLMs)
  • Natural Language Processing (NLP)
  • PyTorch
  • AWS
  • Hugging Face
  • LangChain
  • VectorDBs

Nice to have

  • PhD in STEM field
  • Scala
  • R
  • training optimization
  • self-supervised learning
  • explainability
  • RLHF

What the JD emphasized

  • delivering models at scale both in training data and inference volumes
  • experience in delivering libraries, platforms, or solution level code to existing products

Other signals

  • building and shipping state of the art scalable architecture, AI/ML solutions
  • deliver AI powered products
  • operationalize them in scalable and resilient production systems
  • delivering models at scale both in training data and inference volumes
  • experience in delivering libraries, platforms, or solution level code